Research topic: Understanding the function of mitochondria at the molecular level.

Research methods: The primary research approach that we utilize is reconstitution of a studied system from individual purified proteins. We then employ a wide range of biochemistry and biophysics methods to study the structural properties of the reconstituted complexes, in vitro. Finally, we use functional assays (protein refolding and import) to establish a mechanistic link between the structure and function. In parallel, we study the behavior of protein variants in a living system, using bacterial and yeast as model systems of normal and disease causing proteins.

Research topic: We strive to better understand the mechanisms of autoimmunity and inflammation that underlie neurologic diseases and to translate these laboratory discoveries into new therapies.

Research methods: We take a transdisciplinary approach in our research endeavors using immunologic, genomic, proteomic and metabolomic approaches to study neuroinflammation in pre-clinical (murine) and clinical models. Our research combines state of the art technologies such as single-cell genome-wide sequencing, genomic editing, in-vivo imaging, targeted and un-targeted mass-spectrometry, in-situ molecular imaging and real-time metabolic analysis.

Main projects in the lab include:

Elucidating the immune function of the different glial cells in autoimmune diseases and neurologic disorders.

Investigating the immunometabolic response in the brain.

Studying the crosstalk between the innate and adaptive immune systems in the central nervous system.

Exploring the immunological aspects of the astrocyte-neuron relationship.

Research methods: Computational intelligence methods such as evolutionary computation, artificial neural-networks, fuzzy logic, and their hybridizations.

Main Projects in the lab include:

Neuro-fuzzy Inferencing about Natural Systems – The development of a novel adaptive-neuro-fuzzy inference method for understanding the behavior of biological systems. In collaboration with Prof. Amir Ayali, we have already shown the effectiveness of this generic approach for the case of a marching locust in a swarm.

Multi-payoff Games – In the past utility-based game theory has been used for the understanding of natural systems. In collaboration with Dr. G. Avigad and others, we have developed a non-utility based approach to multi-payoff games (games involving conflicting objectives for each player). We postulate that our recent achievements in defining rationalizable strategies in such games may lead to some new developments in understanding natural systems.

Computational Neuroevolution – We have developed several unique approaches to artificially evolve neural-networks. For example, we suggested a unique algorithm for multi-objective topology and weight evolution of recurrent neural networks. While developed for robotics, we suggest that it may be used for understanding natural systems. Note: The postulations in item 2 and 3 are in accordance with our theory of multi- competence cybernetics. Initial presentation of this theory can be found in: Moshaiov, A. “Multi-competence Cybernetics: The Study of Multi-objective Artificial Systems and Multi- fitness Natural Systems.” In Multiobjective Problem Solving from Nature. Springer Berlin Heidelberg, pp. 285-304, 2008.

Research topic: Studying how ensembles of neurons from different nuclei of the brain (both cortical and sub-cortical) collaboratively process taste information to drive behavior, and how experience adaptively change their activity.